AX (Agent Experience): Optimizing Your Brand for AI Agents

AI agents are reshaping how people access, use, and trust information online. AI agents can summarize content and answer user questions and also take real actions like writing code and navigating websites at a user’s request. As AI agents become a more common element of the internet, brands must consider how AI agents engage with a website. This is the foundation of AX: Agent Experience.

We’ll explore what AX means, why it matters, and how brands can prepare. Through a focused and tactical approach based on our AI expertise, we’ll provide the knowledge you need to ensure LLMs highlight your brand and present accurate information.

Definition of AI Agents

AI agents are the next step toward a fully interactive, and yet simultaneously autonomous internet. After interacting with a user, AI agents can design and execute a step-by-step plan without waiting for the user to ask the next question or provide an additional prompt. 

AI agents can retrieve live content and analyze the information based on the goal they’ve been given. The agent then completes the action and returns the request. Websites must be designed to be readable and usable by these AI agents.

Types of AI Agents

There are many types of AI agents. Some agent types may be more familiar than others. This comparison table summarizes the most important and relevant information about each agent type.

Definition of Agent Experience (AX) 

AX is how AI agents interpret, navigate, and interact with a brand’s digital assets. In contrast to user experience (UX), which focuses on the human experience, and developer experience (DX), which describes the experience of people building the web, AX ensures that AI can comprehend and utilize information on the internet. What’s important to remember is that agents do not see, use, or remember information as humans do. 

For example, ChatGPT’s Operator is an AI agent accessible to all ChatGPT Pro users. Operator uses the GPT-4o vision capabilities combined with a new model: Computer-User Agent (CUA). 

CUA can interact with graphical user interfaces (GUIs), meaning that Operator can click buttons, scroll through text, and skim through menus people see on screens without API integrations. Operator therefore accelerates the collaborative experience with users by reasoning on its own to self-correct and reaching out to the user when it needs assistance. An optimal AX experience can streamline this process. 

AX Use Cases

Product recommendations are an application of AX that benefits consumers and businesses. As of January 2025, 71% of shoppers wanted Gen AI to be integrated into their shopping experience. The seemingly endless options to integrate AI in the shopping experience can include streamlining purchasing decisions and comparing features. Without optimization for AI agents, eCommerce sites risk losing visibility among potential customers.

On the business side, companies can ensure their product pages are structured for AI agents to read the page. Instead of relying on complex filters or scripts, they can incorporate natural language for descriptions, key features, and benefits. Optimizing the AX of product pages and reviews ensures AI agents are able to surface relevant recommendations.

eCommerce may soon evolve into a space where AI agents autonomously assist shoppers. For example, consumers may use AI services for personalized recommendations on what products to compare. Retailers can use AI to evaluate customer preferences and curate product suggestions based on predicted demand. While AI agents enhance the shopping experience, human expertise is still crucial in the refining process and customer support. In this example, AI agents are sales and customer service collaborators. 

As the space develops, additional use cases will emerge across industries. Companies are already integrating AI agents into customer service channels where agents manage initial questions and direct users to resources. The technology is still maturing as AI agents continue to evolve and the scope for AI agent business function applications expands.

Why AX Matters

Most existing web infrastructure was built with UX or DX in mind. Few brands are on the cutting edge of supporting agent comprehension. Without intentional effort to shape what agents can see on a website, AI may misrepresent data, summarize poorly, or exclude information. Outdated and unstructured content may not be utilized by AI agents at all. 

AI agents can reason, plan, and adapt based on goals as a more dynamic and intelligent partner. AI agents allow for additional efficiency and improved customer experiences, and the prevalence of AI agents will only increase in the coming years. It’s predicted that 85% of enterprises will use AI agents in 2025.

AX Optimization

Here are three best practices to ensure that AI agents can use website information: structure content and data, frame information, customize robots.txt and LLM.txt files, and build for visibility and simplicity.  

Structured Data and Content

AI agents rely on structured data and content to process and respond to user inquiries. These two elements serve distinct purposes. 

Structured data refers to information embedded in a website’s HTML code in the form of schema markup. This data helps search engines and AI agents understand and categorize page content. 

Examples of structured data include product schema, event schema, and FAQ schema. By implementing structured data, businesses improve their content’s visibility and relevance in AI responses.

Structured content focuses on how information is presented on a webpage. Well-structured content uses descriptive headings and subheadings to guide readers. With concise answers, structured content is easy to navigate and addresses user intent. Bullet points and lists also make content accessible and readable. 

Information Framing

AI agents operate on models designed for conversational tasks, so clarity is essential. Pages that incorporate semantic HTML, schema markup, and well-organized content structure tend to rank higher in AI answers. Businesses can use schema markup to improve machine readability. They can also organize content with clear headings, summaries, and structured formats. Structured content ensures concise responses that align with user intent.

Build for Visibility and Simplicity

Designing sites that agents can read allows them to understand the content and execute on plans with the information. Developers can enhance the agent experience by implementing progressive enhancement, content rendered on servers, and avoiding excessive dependencies. 

To test visibility, use bot simulators to verify what agents see. Readability.js by Mozilla extracts the main text from web pages. The library analyzes a web page’s HTML structure and removes unnecessary elements. This can simulate what an AI agent might isolate as important. 

While some optimization tactics overlap between AI crawlers and agents, some nuances differentiate the two. AI crawlers index content and data to build an understanding of what exists on a page for AI engines to pull from. AI agents, in comparison, interact with content to carry out tasks, answer questions, or make decisions based on data on the internet. 

While both rely on structured data and clear content, AI agents must engage with content that is framed for conversations and anticipates user questions. Crawlers prioritize indexing efficiency and metadata. Developers should consider the technical readability for crawlers, but also the clarity, intent, and task-based structure for AI agents to respond to user requests.

Monitor and Audit Agent Interactions

AI agents will continue to increase their influence over brand perception as users rely on them to complete tasks. Ensuring that agents effectively navigate your website and access brand information is critical. 

The better agents can navigate websites, the more likely agents are to use the content to carry out their tasks. This is often without direct involvement from your brand. Since accuracy varies across LLMs, maintaining brand integrity involves regularly monitoring how your content shows up and whether AI agents can navigate your website. 

AI optimization tools, such as Goodie, help brands monitor their visibility on AI platforms and identify incorrect summaries or citations. Goodie also pinpoints the variables that affect brand visibility, uncovers ranking patterns, and generates recommendations to boost performance across LLMs, agents, and AI search platforms. 

The platform’s analytics and attribution component measures the impact of LLMs and AI discovery from impressions to revenue. It also attributes value across AI search and agent interactions. Goodie completes the content development and brand visibility loop by identifying content gaps, outlining high-impact material, and writing articles to improve AI search rankings and visibility.

Strategic and Operational Consequences of Ignoring AX

Consumers are already using AI to perform tasks. Nearly half (45%) rely on AI to respond to texts or emails, 43% use it to answer financial questions, and 38% turn to AI for travel planning. AI influences consumer communication, finances, and decision-making processes. 

Ignoring AX means risking exclusion from AI responses that shape consumer perceptions. If AI systems cannot access or accurately interpret messaging, businesses may lose control over how they’re represented. Ensuring brand content is optimized for AI engagement is crucial to maintaining relevance and trust. 

Making Your Brand AX Ready

Immediate Actions

The first step to ensure your brand is AX ready is conducting an internal audit by reviewing your robots.txt and LLM.txt files. Make sure they reflect your content access strategy. Identify which pages should be visible to agents and prioritize verifying that they aren’t blocked.

Next, improve website structure with schema markup on content types that agents frequently rely on: jobs, products, FAQs, and reviews. Ensure HTML is clean and accurate, use proper headings, and avoid hidden content.

Medium-Term Improvements

Monitor how your brand appears over time with tools like Goodie and track summaries, citations, and mentions. Update inaccurate content at its source and create new pages where gaps exist.

Adapt language for clarity, anticipate user questions, and streamline structure. Each page should have a clear purpose and be easy to interpret.

Long-Term Readiness

Over time, brands can support agent development by keeping documentation, component libraries, and test environments open and readable to agents. Beyond APIs, this is about building systems with agents as collaborators.

Commit to ongoing updates and digital operational improvements. AI influences how users discover, evaluate, and engage with brands online. Agents interpret brand content and deliver their understanding to end users in real time. If that experience is broken, misleading, or incomplete, there can be negative brand visibility and image implications.

To improve accurate brand visibility on AI platforms, it’s essential to implement effective optimization. Optimizing for AX involves structuring content, clarifying language, managing access, and adapting to new users.

The next generation of the web will be built for people, agents, and people and agents working together. The experience brands offer to AI will define how people see them. It’s more important than ever to make that experience count.